MIMI Brings OR Tools Together

This article is based on a talk given by Kirk Williams, Managing Director of Chesapeake Decision Sciences Europe Ltd to the Mathematical Programming Study Group.

Requirement for an OR Toolkit

Chesapeake
Decision Sciences was founded in 1982 by Tom Baker, who had been OR
Coordinator in Exxon International for many years. Its first focus was
on the oil industry, and in particular on the problems of planning and
scheduling oil refineries. This remains one of the company's main
business areas, but it has also expanded into other continuous process
industries and into discrete manufacturing.

Throughout
this time it has been extending the capabilities of its product MIMI,
an industrial-strength OR toolkit. This integrates technologies such as
an LP optimiser (CPLEX), an expert system engine and scheduling
algorithms as well as tools for building graphical user interfaces.

The
toolkit approach has been driven by the requirement to solve clients'
problems in general and those of planning and scheduling oil refineries
in particular. This is a problem of enormous complexity where
individual facets can be tackled using a single OR technology but the
entire problem requires a multitude of technologies in combination.

The Refinery Business

Oil
refining has become a commodity business: the products are the same
from everyone. This is literally true: petrol stations are supplied
from their local refinery irrespective of the label on the pump. In
western Europe the market for oil products is saturated and margins are
low. A cargo of crude costs perhaps $20 million and, after refining,
the products will sell for $21 million. Out of the gross margin of $1
million, processing costs will be $200,000 and transport costs
$100,000. The balance has to cover everything from marketing through to
the capital costs of upgrading the plant so that products comply with
new vehicle emission regulations.

Oil
refining is dominated by large companies with vast reservoirs of
accepted wisdom etched in the corporate psyche. This wisdom is almost
always wrong. For instance, an oil company built a retail price
management system which enabled it to increase its market share by 5%.
But did it increase the company's profit? It couldn't tell. It was
pursuing the accepted wisdom that it was good to sell more.

When
Chesapeake builds applications its aim is to help the client change the
way it approaches its business. In refining this means moving away from
managing stocks to "added value management". Systems are designed to
help people make decisions in terms of their economic consequences.
Models exist within a framework of setting targets, taking actions,
monitoring results and using those to reassess targets.

Planning and Scheduling

Planning
and scheduling in refineries takes place over a hierarchy of time
horizons. At the top level there is enterprise planning: this is
concerned with a company's market position worldwide and allocating
capital investment over a period of 5 years or more. Below this is
operational planning over time horizons between 1 week and 6 months;
this is concerned with deciding which crudes to buy, how to process
them and which products to sell. At the bottom there is detailed
scheduling within the refinery, which answers the question "What am I
going to do next?". The cascade of models used in operational planning
and scheduling is shown in Figure 1.

Figure 1: Planning and Scheduling Cascade in a Refinery

Linear
and Integer Programming are heavily used in the longer-term planning
models. With shorter time horizons the models have to be more detailed
and accurate and this leads to the use of Successive Linear
Programming. The greatest challenges lie with the transition from
operational planning to detailed scheduling, where the assumptions
implicit in LP-based models break down. These are that operations can
be broken down into a series of time periods, during each of which it
suffices to model activities as continuous (or average) flows.

Disaggregating an LP Plan

The
shortest time horizon over which LP-based models are normally used is 1
- 2 weeks. Such a model might have 3 - 5 time periods with the first
time period typically 1 - 2 days. The model will be "rolled forward"
every 1 - 2 days, i.e. rerun with updated data to describe the problem
which the refinery now faces.

Such a
model provides useful guidance to how to run the main process units,
but it does not address the logistical issues of what is happening on
the tanks and how to sequence batch activities. Chesapeake has been
tackling this problem with a combination of Mixed Integer Programming,
expert system rules and a planning board. Mixed Integer Programming is
used first to do some disaggregation; then expert system rules are used
to extract a first-cut schedule. This is displayed on a graphical
planning board (Figure 2) which highlights problems, e.g. stock over-
or underflows.

Figure 2: MIMI Planning Board for Refinery Scheduling

Using
his knowledge of the refinery, the operator then seeks to overcome the
problems by manipulating the schedule on the planning board. As he does
so, the planning board assists him by tracking the consequences of
changes and displaying them in real time.

Man and Machine

Refinery scheduling staff sometimes say that they would like a completely automatic scheduling package which generated the
best schedule. This is not a realistic goal with current technology,
nor is it a good idea. What is needed is synergy between man and
machine.

The
machine should filter the enormous number of possible solutions and
present a small number of good ones to the man. The man should then
select the best given his understanding of what is really happening on
the plant. For instance, at one refinery the loading bay tended to
flood to a depth of a couple of inches after heavy rain. This wouldn't
have been a problem if it hadn't been for the dog which lived there.
The dog became grumpy and loading had to be suspended until the dog had
been pacified.

Conclusion

Hard
scheduling problems are never going to be solved completely. The aim
should be to achieve 80-20 solutions, in which the machine does 80% of
the work (and the donkey work at that) and the man 20%. In doing this
it will always be necessary to mix and match solution techniques such
as LP, scheduling algorithms and expert system rules. MIMI is an
integrated set of tools which has been proved over the years in
tackling some of the most demanding such applications.